Seizing AI opportunities

Over the din of the lunch hour cafeteria, there was a shimmer in the air − a sense that something great was being discovered. This was different than a normal lunchtime at Los Alamos National Laboratory in 1950 where the Cold War had mobilized the West’s brightest minds.

During a normal lunchtime one could expect breakthroughs in particle physics or in fusion power, but there was a buzz today that could not be attributed to the Chicken Kiev. Three great minds were at work solving one of life’s great existential questions − are we alone in the universe?

It was a frenzy. A crowd began to gather − many aware of the recent reports of UFO sightings nearby. Finally, as the calculations poured in and the Coke bottles slurped the last gasp of soda, the math was obvious – there must be intelligent extraterrestrial life in the universe − the vastness of the universe assured the outcome to be true.

BAM! The hand of an Italian-American physicist from the University of Chicago slammed the table. The “architect of the nuclear age” was troubled, the conclusion did not make sense to Enrico Fermi − “But where is everybody?”

The contradiction between the obvious math and the lack of a conclusion has become known as Fermi’s Paradox – if intelligent extraterrestrial life is such an obvious conclusion, then where is it?

Just as those scientists looked to the stars for signs of intelligent life, investing has for decades looked to computers and quantitative methods for signs of Artificial Intelligence that can help make smarter decisions. But after decades, finance is confronted with a similar paradox.

There is a persistent dream of putting an AI-driven version of Warren Buffett in every investment team, one with all the positive qualities but none of the negative biases and behavioral errors that come pre-installed in humans.

The excitement of building such a revolutionary computer-based system to pick investments has driven billions of dollars of investment into building systems and hiring big-brained PhDs. The share of job openings in finance that are computer or math driven has nearly quadrupled since the Great Financial Crisis.

But most actively managed assets are still non-quantitative in nature Despite all the investments, decades of academic papers, computer systems, and fortunes made in quant investing, the vast majority of actively managed assets are still non-quantitative in nature.

If one stops to think about what makes a great investor, it’s not typically a niche task specific process that differentiates the legends from the temporarily lucky.

Because markets are complex systems whose dancing landscapes are constantly changing, the best investors are generalists by nature. They take mental models and are able to apply them over and over again.

They do not merely learn facts, rather they learn models and systems which they can reach into their toolkit and apply when appropriate.

The computational complexity is low and the objective is to handicap all possible outcomes – to discount the implied market not to forecast. They think about what investments present asymmetric payouts from a probabilistic perspective in a folksy back-of-the-envelope manner.

To build AI that can successfully be implemented in investing, we must align the design of the machine with the cognitive tasks of great investors.

Our team at UBS-AM, called Quantitative Evidence and Data Science, has taken the approach of focusing on investor workflows as a guiding principle – we want to understand what are the things that investors do in order to help them form the investment mosaic to help them make a decision.

In the next several years, QED will be spending more and more time focusing on how to generalize these workflows and to combine them with heuristics (problem solving techniques to generally use self-educating and trial and error methods) to form investment conclusions.

Our goal is to create a form of Artificial General Intelligence (AGI) that can apply reasoning to identify and apply mental models hidden in novel problems and then to, ultimately, make an investment recommendation.

QED has focused on aligning our machines with real investment work- flows. In the next year we will focus on an effort to generalize those workflows so that ultimately the machine can make real investment recommendations.

This may seem to be an audacious goal; however, the process to get there is the best way for us to help drive science into the fundamental investment process.

As we solve problems in the path towards AGI, we can directly apply the solutions into the investment workflows.